Method for Analysing the Rules of Changes Between the Levels of Use of Resources of a Computer System

ABSTRACT

A method for evaluating the performance of an application chain within a computer infrastructure comprising a number N resources denoted R i  (1≤i≤N), where the method comprises the steps of: collecting over a same time interval with a same sampling period a predefined number M of series of measurements X k  (1≤k≤M) relating to the level of use of the resources; for all the possible combinations of two series of measurements (X k1 , X k2 ), with k1≠k2: creating a plurality of pairs of subsets (X′ k1 , X′ k2 ) by selecting a predefined number n v  of values based on the series X k1  and X k2 ; applying an algorithm for searching affine correlation relation(s) over each pair of subsets; calculating the percentages differences between the values of X′ k2 (t) and of aX′ k1 (t)+b for each index t (between 1 and n v ); and calculating the saturation values of the series X′ k2 .

The present invention relates to the field of monitoring an IT(Information Technology) infrastructure, this expression denoting allthe hardware and software elements forming the computer system of acompany or organization. The invention relates more particularly to thefield of analyzing resources (notably processors, operating systems andmemories) of an IT infrastructure on which there is hosted anapplication link chain, i.e. for a process, a functional chainconnecting several applications which operate together to perform theprocess.

A number of IT infrastructures are poorly dimensioned, and most oftenunder-dimensioned. Poor dimensioning results in inadequate performance,or even stopping of production. Correctly dimensioning an ITinfrastructure is a major challenge for companies for which productiondepends on the performance of their IT systems. The term “dimensioning”denotes the capacities (computational and memory) of the servers,coupled with the availability of resources (hardware and software).

An increase in the load of an IT system can be accompanied by a gradualsaturation of the resources of the system within the same functionalchain (or application link chain). The saturation of a resource blocksthe increase in the load of the system and therefore prevents theobservation of possible saturation of other resources in the chain.

The use of a resource can bring about the use of another resource. Byway of example, in the case of an application ordering a calculation tobe performed on a machine A and its result to be saved on a machine B,the level of use of the processors of machine A depends on the progressof the save operations on machine B.

Each resource is characterized by a maximum level of use for optimalfunctioning (for example, twenty-four percent for a processor).

The present invention aims to propose a method for defining acorrelation of the level of use of a resource A with respect to thelevel of use of a resource B in order to determine, when resource B issaturated and resource A is not, the dimensioning of resource B requiredto reach the maximum level of resource A.

The objective is to dimension, coherently and optimally, the resourcesof an IT system and prevent the resources from saturating and theconsequences thereof.

The search for correlations in the changes of the levels of use of theresources of an application chain aims to predict:

-   -   the change in consumptions and the saturations of the resources        when the load is increased,    -   the dimensioning of the resources of an application chain        comprising several servers.

Solutions exist for monitoring servers individually, but they do notprovide for determining the levels of future use of resources, norestablishing a correlation between the various levels of use ofresources of different servers within the same application chain.

An objective of the present invention is to enable an automatic analysisof the consumption of resources of an IT system and derive therefromcorrelations between the levels of use of the resources.

To this end, there is proposed a method for evaluating the performanceof an application chain within an IT infrastructure, comprising a numberN of resources R_(i) (where i is an integer between 1 and N), comprisingthe steps of:

-   -   collection, over the same time interval and with the same        sampling period period_(ech) of a predefined number M of series        of measurements X_(k) (where k is an integer between 1 and M)        relating to the levels of use of different resources,    -   for all possible combinations of two series of measurements        (X_(k1),X_(k2)), where k1≠k2, among the collected series:        -   creation of several pairs of subsets (X′_(k1),X′_(k2)) by            selecting a predefined number n_(v) of values from the            series of measurements X_(k1) and X_(k2) respectively,        -   application of an affine correlation relationship search            algorithm on each pair of subsets (X′_(k1),X′_(k2)), the            affine correlation being modeled by the equation            X′_(k2)=aX′_(k1)+b, where a and b are real numbers,        -   calculation, for each pair (X′_(k1),X′_(k2)), of the            percentages P(t) of the difference between the values of            X′_(k2) (t) and of aX′_(k1)(t)+b according to the formula

${{P(t)} = {100{\frac{{X_{k\; 2}^{\prime}(t)} - ( {{{aX}_{k\; 1}^{\prime}(t)} + b} )}{X_{k\; 2}^{\prime}(t)}}}},$

-   -   -    at each index t (between 1 and n_(v)),        -   calculation, for each pair (X′_(k1),X′_(k2)), and provided            that all the values of P(t) are less than or equal to a            predefined value T, of saturation values

${X_{k\; 1{smin}}^{\prime} = {{\frac{X_{k\; 2m\; i\; n}^{\prime} - b}{a}\mspace{14mu} {and}\mspace{14mu} X_{k\; 1{sm}\; {ax}}^{\prime}} = \frac{X_{k\; 2{ma}\; x}^{\prime} - b}{a}}},$

-   -   -    where X′_(k2 min) and X′_(k2 max) are respectively the            minimum and maximum values of the series of measurements            X′_(k2).

According to various characteristics taken alone or in combination:

-   -   the value of n_(v) is between 3 and 60.    -   each series of measurements is carried out over a time interval        greater than or equal to two hours.    -   each series of measurements is carried out with a sampling        period period_(ech) of one minute.    -   the value T is 95%.    -   the number of pairs of subsets is between 1 and 100.

The step for selecting the subsets X′_(k1) and X′_(k2) includes theoperations of:

-   -   taking into account the following parameters: the minimum values        p_(min) and maximum values p_(max) of a search period denoted by        p, where p is a variable of the method, the increment size        p_(pas) of the period p, a sampling period period_(ech),    -   creation of the n_(v) values of the subset X′_(k1) by selecting        n_(v) values in the series X_(k1),    -   creation of the n_(v) values of the subset X′_(k2) by selecting        n_(v) values in the series X_(k2),

The algorithm for searching for an affine relationship between twoseries of measurements X′_(k2) and X′_(k1) comprises the operations of:

-   -   calculation of a as being the ratio between X′_(k2moy) and        X′_(k1moy), i.e.

${a = \frac{X_{k\; 2{moy}}^{\prime}}{X_{k\; 1{moy}}^{\prime}}},$

-   -    where X′_(k2moy) is the average of the differences between the        successive values in the list X′_(k2), i.e.

$X_{k\; 2{moy}}^{\prime} = {\frac{1}{n_{v} - 1}{\sum\limits_{t = 2}^{n_{v}}( {{X_{k\; 2}^{\prime}(t)} - {X_{k\; 2}^{\prime}( {t - 1} )}} )}}$

-   -    and X′_(k1moy) is the average of the differences between        successive values in the list X′_(k1) i.e.

${X_{k\; 1{moy}}^{\prime} = {\frac{1}{n_{v} - 1}{\sum\limits_{t = 2}^{n_{v}}( {{X_{k\; 1}^{\prime}(t)} - {X_{k\; 1}^{\prime}( {t - 1} )}} )}}},$

-   -   calculation of b according to the formula

$b = \frac{a( {\sum\limits_{i = 1}^{n_{v}}( {{X_{k\; 2}^{\prime}(t)} - {X_{k\; 1}^{\prime}(t)}} )} }{n_{v}}$

-   -    where X′_(k2)(t) and X′_(k1)(t) are the values in the series        X′_(k2) and X′_(k1) at the index t.

According to various characteristics taken alone or in combination:

-   -   the parameter p_(min) is fixed at a value between 1 and 10.    -   the parameter p_(max) is fixed at a value between 1 and 100.    -   the parameter p_(pas) is fixed at a value between 1 and 10.

The invention will be better understood and other details, features andadvantages of the invention will emerge from reading the followingdescription, given by way of nonlimiting example with reference to thedrawings in which:

FIG. 1 is a schematic representation of five resources and possiblecombinations between the series of measurements carried out on theseresources.

FIG. 2 is a functional diagram illustrating various steps of the methodfor searching for rules of changes between the various resources of anIT system.

FIG. 3 is a pseudocode describing an example embodiment of the method inthe case of searching for a rule of change between two series ofmeasurements.

An IT architecture (or system, or infrastructure) conventionallycomprises various hardware and/or software resources which, to performprocesses, are connected to each other to form one or more functionalchains (or application link chains, or application chains).

To optimize the operation of such an application chain, its performanceand notably the use of the resources forming it must be evaluated. N(where N is an integer) denotes the number of resources, denoted byR_(i) (where i is an integer such that 1≤i≤N), of the application chain.

To evaluate the performance of the application chain, the principle isto search for the rules of change between several series of measurementsperformed on the resources, typically the level of use, load, availablememory, occupied disk space or memory. “Rule of change” is understood tomean an affine type correlation relationship between two series ofmeasurements relating to levels of use of resources R_(i). FIG. 1provides an example of five resources R_(i) (1≤i≤N, N=5), denoted by R₁to R₅.

One step in the method involves performing and collecting a plurality ofseries of measurements denoted by X_(k), each measurement supplying alevel (or rate) of use of a resource R_(i). These series are denoted byX₁ to X₅ in the example of FIG. 1. The level of use of a resource is aphysical quantity, the nature of which can vary according to the type ofresource examined. It can be the power consumed in the case of aprocessor (for example, a central processing unit), a percentage of themaximum transfer rate in the case of a hard disk, or a percentage of thetotal capacity (or occupation rate) in the case of random access memory.

FIG. 2 illustrates the main steps of the method.

A preliminary step consists in collecting a predefined number M (where Mis an integer not necessarily equal to N) of series of measurementsX_(k)(1≤k≤M) carried out over the same time interval and with the samesampling period denoted by period_(ech).

The measurements are advantageously carried out automatically by aprogram executed on one or more servers incorporated in the ITinfrastructure.

The measurements are preferably performed (and collected) over a timeinterval of at least two hours, with a sampling period of one minute. Byway of example, the measurements are carried out over a period of fourhours (typically between 08:00 and 12:00), with a sampling period of oneminute (i.e. two successive measurements are spaced out by one minute).

The measurements provide, for example, for determining the level ofactivity of a central processing unit (CPU) and disks of two servers. Inthis example, the method proposed by the present invention provides fordetermining affine type correlations between the activities ofprocessors and disks of two servers, in all possible combinations:

-   -   correlation between the level of activity of the CPU of the        first server and that of its own disk,    -   correlation between the level of activity of the CPU of the        first server and that of the disk of the second server,    -   correlation between the level of activity of the CPU of the        second server and that of its own disk,    -   correlation between the level of activity of the CPU of the        second server and that of the disk of the first server,    -   correlation between the level of activity of the CPU of the        first server and that of the CPU of the second server,    -   correlation between the level of activity of the disk of the        first server and that of the disk of the second server,

A series of measurements can be the result of one measurement or thecombining of the results of several measurements carried outsimultaneously. For example, a series of measurements can contain thesum of the data rates of all the disks present on the machine.

The correlation search method proposed by the present invention aims,for a set of series of measurements collected, to establish correlationrelationships between different pairs of series of measurements denotedby (X_(k1),X_(k2)) (where k1 and k2 are integers between 1 and M andwhere k1≠k2) from the collected measurements. Each pair of series ofmeasurements corresponds to a particular combination of two series ofmeasurements. In the example of FIG. 1, if a series of measurementsX_(k) is collected for each resource R_(i), i.e. each series ofmeasurements X_(k) corresponds to the level of use of a resource R_(i),then there will be 10 possible pairs of series of measurements denotedby 1 to 10. Recall that an objective of the present invention is thedetermination of correlation relationships for all possible combinationsof two series of measurements.

A first step consists in selecting two series of measurements X_(k1) andX_(k2) from the set of series of measurements collected.

A second step consists in searching for an affine correlationrelationship over at least n_(v) values (where n_(v) is an adjustableinteger) between the two series of measurements X_(k1) and X_(k2). Thisaffine correlation relationship is illustrated by equation (1):

X _(k2) =aX _(k1) +b  (1)

-   -   where a and b are real numbers.

Percentages P(t) of the difference between the values X_(k2)(t) andaX_(k1)(t)+b are calculated, X_(k2)(t) referring to the value of themeasurement of index t in the series X_(k2), and X_(k1)(t) referring tothe value of the measurement of index t in the series X_(k1). Thiscalculation is illustrated by equation (2), these percentages beingdefined as follows:

$\begin{matrix}{{P(t)} = {100{\frac{{X_{k\; 2}(t)} - ( {{{aX}_{k\; 1}(t)} + b} )}{X_{k\; 2}(t)}}}} & (2)\end{matrix}$

-   -   where t is an integer index such that 1≤t≤n_(v).

If each value of P(t) obtained is less than or equal to a predefinedvalue T, for example fixed by an operator (typically the networkadministrator), then the affine correlation relationship (1) isvalidated and saved. T is called the tolerance percentage and isadvantageously fixed at 95%. According to a preferred embodiment, n_(v)is advantageously between 3 and 60.

In this case, the method comprises a next step for calculatingsaturation values X_(k1s min) and X_(k1s max) for the series ofmeasurements X_(k1) using the following formulas (3) and (4):

$\begin{matrix}{X_{k\; 1{smin}} = \frac{X_{k\; 2m\; i\; n} - b}{a}} & (3) \\{X_{k\; 1{smax}} = \frac{X_{k\; 2{ma}\; x} - b}{a}} & (4)\end{matrix}$

-   -   where X_(k2 min) and X_(k2 max) are the minimum and maximum        values, respectively, of the series of measurements X_(k2). If        at least one of the values X_(k1s min) or X_(k1s max) belongs to        the interval ]X_(k1 min),X_(k1 max)[ where X_(k1 min) and        X_(k1 max) are the minimum and maximum values of the series        X_(k1), then the rule of change found is such that the resource        associated with the series of measurements X_(k2) will saturate        before the resource associated with the series of measurements        X_(k1). More specifically, the resource X_(k2) will begin to        saturate when the resource X_(k1) comes close to the value of        X_(k1s min).

If no correlation relationship has been found, an additional stepconsists in processing the next combination of series of measurements,this step being repeated until all the possible combinations have beenanalyzed. One variant consists in carrying out this same process for amultitude of pairs of subsets (X′_(k1),X′_(k2)) obtained from a pair ofseries of measurements (X_(k1),X_(k2)). In this case, the series X′_(k1)is obtained by selecting a predefined number n_(v) of values in theseries X_(k1). Likewise, X′_(k2) is obtained from X_(k2).

FIG. 1 illustrates an example in which the correlation relationship iscalculated directly on the series of measurements X_(k1) and X_(k2),thereby corresponding to the particular case in which n_(v) is equal tothe number of values contained in each series X_(k1) or X_(k2). Avariant of the method consists in calculating correlation relationshipson subsets (X′_(k1),X′_(k2)) obtained from a pair of series ofmeasurements (X_(k1),X_(k2)) as indicated earlier. This possibility isoffered to the user by proposing an initial configuration illustrated inFIG. 3. This example is provided for an example pair of series ofmeasurements denoted by (X_(k1),X_(k2)). The same steps are applied onall the possible combinations of series of measurements (X_(k1),X_(k2))from the collected data.

The parameters that can be adjusted by the user are:

-   -   [X_(k1deb),X_(k1fin)]: an interval for searching values,    -   [Y_(k2deb),Y_(k2fin)]: an interval for searching values,    -   p_(min): the minimum value of variable p corresponding to a        period for selecting subsets X′_(k1) and X′_(k2),    -   p_(max): the maximum value of the period p,    -   p_(pas): the increment size for the period p,    -   n_(v): the number of values in each subset X′_(k1) and X′_(k2),    -   T: the tolerance percentage for the validation of a correlation        relationship between the series X′_(k1) and X′_(k2).

For the particular case in which the values of p_(min), p_(max) andp_(pas) are 1, the search intervals cover all the values of X_(k1) andX_(k2), and n_(v) is equal to the size of the sequence X_(k1) and to thesize of X_(k2). The value of p will then be 1 and the subsets X′_(k1)and X′_(k2) will be the same as the initial series X_(k1) and X_(k2).The search is then performed directly on the series of measurementsX_(k1) and X_(k2).

To construct a subset X′_(k1) from X_(k1), the operation consists inselecting a value on n_(s) in X_(k1) and in incorporating it into thesubset X′_(k1). For example, if n_(s) is 2, a value on 2 will beselected in X_(k1) to construct X′_(k1).

If for example p_(min) is 1, p_(max) is 8 and p_(pas) is 2, then thevalues of the variable n_(s) will successively be 2, 4, 6 and 8. Thisresults in four pairs of subsets (X′_(k1),X′_(k2)) for which acorrelation relationship will be sought. A correlation relationship isfound for the series X_(k1) and X_(k2) if correlation relationships arefound for all the pairs of subsets (X′_(k1),X′_(k2)) generated. Ifduring the process, a correlation relationship is not found for at leastone pair of subsets, then no correlation is generated between the seriesof measurements. In that case, a new combination of series ofmeasurements (X′_(k1),X′_(k2)) is selected in the collected data and theprocess is restarted.

The benefit of working on pairs of subsets (X′_(k1),X′_(k2)) of pairs ofseries of initial measurements (X_(k1),X_(k2)), and not directly on theseries of initial measurements is to provide an indicator of therelevance of the correlation found. Specifically, for a pair of seriesof measurements (X_(k1),X_(k2)), and provided that correlations arefound for all the subsets (X′_(k1),X′_(k2)) generated, the greater thenumber of subsets, the stronger the correlation relationship between theseries of measurements X_(k1) and X_(k2). According to a preferredembodiment, the number of pairs of subsets used is between 1 and 100.

The variation in the sampling period p, between p_(min) and p_(max),provides for taking into account only the extreme values (high or low,for example in the case of a series of measurements representing asinusoidal curve).

At the end of this step, a pair of two subsets X′_(k1) and X′_(k2) isobtained, each containing n_(v) values.

An affine type correlation equation is sought between these two subsets.It can be expressed as in equation (1): X′_(k2)=aX′_(k1)+b.

The value of a is calculated by calculating the ratio between theaverage X′_(k2moy) of the differences between the successive values inthe list X′_(k2) and the average X′_(k1moy) of the differences betweenthe successive values in the list X′_(k1). The calculation of a isillustrated by equation (5):

$\begin{matrix}{{a = \frac{X_{k\; 2{moy}}^{\prime}}{X_{k\; 1{moy}}^{\prime}}},} & (5)\end{matrix}$

The calculations of the average values X′_(k2moy) and X′_(k1moy) areillustrated by equations (6) and (7):

$\begin{matrix}{X_{k\; 2\; {moy}}^{\prime} = {\frac{1}{n_{v} - 1}{\sum\limits_{t = 2}^{n_{v}}( {{X_{k\; 2}^{\prime}(t)} - {X_{k\; 2}^{\prime}( {t - 1} )}} )}}} & (6) \\{X_{k\; 1{moy}}^{\prime} = {\frac{1}{n_{v} - 1}{\sum\limits_{t = 2}^{n_{v}}( {{X_{k\; 1}^{\prime}(t)} - {X_{k\; 1}^{\prime}( {t - 1} )}} )}}} & (7)\end{matrix}$

The calculation of the value of b is illustrated by equation (8):

$\begin{matrix}{b = \frac{a( {\sum\limits_{t = 1}^{n_{v}}( {{X_{k\; 2}^{\prime}(t)} - {X_{k\; 1}^{\prime}(t)}} } }{n_{v}}} & (8)\end{matrix}$

where X′_(k2)(t) and X′_(k1)(t) are the respective values in the seriesX′_(k2) and X′_(k1) at index t.

The next step is the test for the reliability of the correlationrelationship thus generated. To that end, an example embodiment consistsin generating a list Z from n_(v) values of the list X′_(k1) in whicheach value Z(t) is connected to the value X′_(k1)(t) by the affinecorrelation relationship (1): Z(t)=aX′_(k1)(t)+b. Each percentage P(t)of the difference between the values Z(t) and X′_(k2)(t) is calculated,as illustrated in equation (9):

$\begin{matrix}{{P(t)} = {100{\frac{{X_{k\; 2}^{\prime}(t)} - {Z(t)}}{X_{k\; 2}^{\prime}(t)}}}} & (9)\end{matrix}$

The step consisting in generating a list Z(t) is an intermediate stepwhich is not indispensable for calculating the percentage P(t), whichcan be calculated directly as shown by equation (2). This step generatesa sequence of percentages that can be denoted by P and which containn_(v) values denoted by P(t).

If at least one value of P(t) is strictly greater than the tolerancepercentage T, then there is no correlation between the series ofmeasurements X′_(k1) and X′_(k2), and the search algorithm processes thenext combination of series of measurements. If among a multitude ofpairs of subsets (X′_(k1),X′_(k2)), one among them does not provide acorrelation equation, then it is considered that there is no correlationbetween the series of measurements X_(k1) and X_(k2) (from which thesubsets were generated).

If all the values of P(t) are less than or equal to the tolerancepercentage T, then the correlation equation X′_(k2)=aX′_(k1)+b isvalidated for the pair of subsets X′_(k1) and X′_(k2). In that case, thenext step is calculating the saturation values X′_(k1s min) andX′_(k1s max) in the same way as in equations (3) and (4), replacingX_(k2 min) and X_(k2 max) by X′_(k2 min) and X′_(k2 max), and asillustrated in FIG. 3.

If a correlation relationship is found for each pair of subsets(X′_(k1),X′_(k2)), then a correlation exists between the initial seriesof measurements X_(k1) and X_(k2). The final values of a, b and thesaturation values X_(k1s min) and X_(k1s max) are obtained bycalculating the average of the values obtained for the subsetsexhibiting a correlation.

Thus, this method provides for generating correlation relationshipsbetween several series of measurements, which may be used to define abetter dimensioning of production infrastructures.

1. A method for evaluating the performance of an application chainwithin an IT (Information Technology) infrastructure, comprising anumber N of resources R_(i) (where i is an integer between 1 and N),comprising the steps of: collection, over the same time interval andwith the same sampling period period_(ech) of a predefined number M ofseries of measurements X_(k), where k is an integer between 1 and M,relating to the levels of use of different resources, for all possiblecombinations of two series of measurements (X_(k1),X_(k2)), where k1≠k2,among the collected series: creation of several pairs of subsets(X′_(k1),X′_(k2)) by selecting a predefined number n_(v) of values fromthe series of measurements X_(k1) and X_(k2) respectively, applicationof an affine correlation relationship search algorithm on each pair ofsubsets (X′_(k1),X′_(k2)), this affine correlation being modeled by theequation X′_(k2)=aX′_(k1)+b, where a and b are real numbers,calculation, for each pair (X′_(k1),X′_(k2)), of the percentages P(t) ofthe difference between the values of X′_(k2)(t) and of aX′_(k1)(t)+baccording to the formula${{P(t)} = {100{\frac{{X_{k\; 2}^{\prime}(t)} - ( {{{aX}_{k\; 1}^{\prime}(t)} + b} )}{X_{k\; 2}^{\prime}(t)}}}},$ at each index t (between 1 and n_(v)), calculation, for each pair(X′_(k1),X′_(k2)), and provided that all the values of P(t) are lessthan or equal to a predefined value T, of saturation values${X_{k\; 1{smin}}^{\prime} = {{\frac{X_{k\; 2m\; i\; n}^{\prime} - b}{a}\mspace{14mu} {and}\mspace{14mu} X_{k\; 1{smax}}^{\prime}} = \frac{X_{k\; 2m\; {ax}}^{\prime} - b}{a}}},$ where X′_(k2 min) and X′_(k2 max) are respectively the minimum andmaximum values of the series of measurements X′_(k2).
 2. The method asclaimed in claim 1, characterized in that the value of n_(v) is between3 and
 60. 3. The method as claimed in claim 1, characterized in thateach series of measurements is carried out over a time interval greaterthan or equal to two hours.
 4. The method as claimed in claim 1,characterized in that each series of measurements is carried out with asampling period period_(ech) of one minute.
 5. The method as claimed inclaim 1, characterized in that the value T is 95%.
 6. The method asclaimed in claim 1, characterized in that the number of pairs of subsetsis between 1 and
 100. 7. The method as claimed in claim 1, characterizedin that the selection of the subsets X′_(k1) and X′_(k2) includes theoperations of: taking into account the following parameters: the minimumvalues p_(min) and maximum values p_(max) of a search period denoted byp, where p is a variable of the method, the increment size p_(pas) ofthe period p, a sampling period period_(ech), creation of the n_(v)values of the subset X′_(k1) by selecting n_(v) values in the seriesX_(k1), creation of the n_(v) values of the subset X′_(k2) by selectingn_(v) values in the series X_(k2).
 8. The method as claimed in claim 7,characterized in that the parameter p_(min) is fixed at a value between1 and
 10. 9. The method as claimed in claim 7, characterized in that theparameter p_(max) is fixed at a value between 1 and
 100. 10. The methodas claimed in claim 7, characterized in that the parameter p_(pas) isfixed at a value between 1 and
 10. 11. The method as claimed in claim 1,characterized in that the algorithm for searching for an affinerelationship between two series of measurements X′_(k2) and X′_(k1)comprises the operations of: calculation of a as being the ratio betweenX′_(k2moy) and X′_(k1moy), i.e.${a = \frac{X_{k\; 2{moy}}^{\prime}}{X_{k\; 1{moy}}^{\prime}}},$ where X′_(k2moy) is the average of the differences between thesuccessive values in the list X′_(k2), i.e.$X_{k\; 2{moy}}^{\prime} = {\frac{1}{n_{v} - 1}{\sum\limits_{t = 2}^{n_{v}}( {{X_{k\; 2}^{\prime}(t)} - {X_{k\; 2}^{\prime}( {t - 1} )}} )}}$ and X′_(k1moy) is the average of the differences between the successivevalues in the list X′_(k1) i.e.${X_{k\; 1{moy}}^{\prime} = {\frac{1}{n_{v} - 1}{\sum\limits_{t = 2}^{n_{v}}( {{X_{k\; 1}^{\prime}(t)} - {X_{k\; 1}^{\prime}( {t - 1} )}} )}}},$calculation of b according to the formula$b = \frac{a( {\sum\limits_{i = 1}^{n_{v}}( {{X_{kz}^{\prime}(t)} - {X_{k\; 1}^{\prime}(t)}} )} }{n_{v}}$ where X′_(k2)(t) and X′_(k1)(t) are the values in the series X′_(k2)and X′_(k1) at the index t.